NiftyNet 0.2.0.post1

Features

NiftyNet currently supports medical image segmentation and generative adversarial networks. NiftyNet is not intended for clinical use. Other features of NiftyNet include:

• Easy-to-customise interfaces of network components

• Sharing networks and pretrained models

• Support for 2-D, 2.5-D, 3-D, 4-D inputs [2]

• Efficient discriminative training with multiple-GPU support

• Implementation of recent networks (HighRes3DNet, 3D U-net, V-net, DeepMedic)

• Comprehensive evaluation metrics for medical image segmentation

Getting started

Citing NiftyNet

If you use NiftyNet in your work, please cite Li et. al. 2017:

Li W., Wang G., Fidon L., Ourselin S., Cardoso M.J., Vercauteren T. (2017) On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. In: Niethammer M. et al. (eds) Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science, vol 10265. Springer, Cham. DOI: 10.1007/978-3-319-59050-9_28

BibTeX entry:

@InProceedings{niftynet17,
  author = {Li, Wenqi and Wang, Guotai and Fidon, Lucas and Ourselin, Sebastien and Cardoso, M. Jorge and Vercauteren, Tom},
  title = {On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task},
  booktitle = {International Conference on Information Processing in Medical Imaging (IPMI)},
  year = {2017}
}

Licensing and copyright

Copyright 2017 University College London and the NiftyNet Contributors. NiftyNet is released under the Apache License, Version 2.0. Please see the LICENSE file in the NiftyNet source code repository for details.

Acknowledgements

This project is grateful for the support from the Wellcome Trust, the Engineering and Physical Sciences Research Council (EPSRC), the National Institute for Health Research (NIHR), the Department of Health (DoH), University College London (UCL), the Science and Engineering South Consortium (SES), the STFC Rutherford-Appleton Laboratory, and NVIDIA.